Value in Health最新文献

Objectives: We investigated how the Inflation Reduction Act (IRA) Medicare Part D benefit redesign may reduce out-of-pocket (OOP) drug expenditures for Medicare beneficiaries with dementia.

Methods: Using data from the Health and Retirement Study (HRS) linked with Medicare claims, we simulated post-redesign OOP drug spending by applying the 2025 prescription drug cost-sharing rules to each beneficiary's pre-redesign Part D medication utilization for 2016, adjusting for inflation. Our study population comprised HRS respondents aged 65 and older in 2016, enrolled in Medicare fee for service, with at least one Part D drug claim in 2016, and diagnosed with dementia between 2000 and 2016 (n = 1677). We compared pre-redesign and post-redesign annual OOP drug expenditures stratified by: (1) low-income subsidy (LIS) eligibility status; (2) household income among non-LIS beneficiaries; (3) comorbidity count, and 4) cognitive impairment severity.

Results: After the redesign, we project average annual OOP drug expenditures among LIS beneficiaries with dementia to decrease from $56 to $25-representing a $31, or 55%, reduction. In contrast, among non-LIS beneficiaries, average OOP drug expenditures is projected to decrease from $772 to $576, a $196, or 25% reduction. We project the reduction in OOP drug expenditures to be greater among beneficiaries with 6 to 8 comorbidities (45%) than among beneficiaries with fewer comorbidities (21%-26%).

Conclusions: The Inflation Reduction Act Medicare Part D benefit redesign will reduce OOP drug spending for beneficiaries with dementia, resulting in potentially marked savings not only for low-income beneficiaries and beneficiaries with a high comorbidity burden but also for higher-income beneficiaries.

Objectives: To provide an overview of policy initiatives in high-income countries aimed at supporting the development and accessibility of treatments for rare diseases.

Methods: We examine how legislative, research, and pricing policies in high-income countries address barriers that have historically hindered innovation and access to rare disease treatments. By analyzing examples from the European Union, United Kingdom, United States, Canada, Japan, and Australia, the article identifies ongoing initiatives, outlines current challenges, and explores proposed solutions to foster a sustainable, innovative, and accessible rare disease treatment ecosystem.

Results: The review highlights policies such as legislative incentives in the European Union, United States, and Japan for orphan drug development, public-private partnerships to boost innovation, and patient registries to support research and clinical trials. Despite these efforts, major challenges persist, including high therapy costs, limited access to innovation for ultrarare diseases, and diagnostic delays, with significant disparities across regions.

Conclusions: Overcoming these challenges will require sustainable pricing and reimbursement frameworks, alongside stronger collaboration between stakeholders, particularly for ultrarare diseases. Advanced technologies, such as artificial intelligence, hold promise for improving diagnostic accuracy and data collection, supported by enhanced coding systems and registries to facilitate more robust research.

Objectives: Cost-effectiveness analysis (CEA) is an accepted approach to evaluate cancer screening programs. CEA estimates partially depend on modeling methods and assumptions used. Understanding common practice when modeling cancer relies on complete, accessible descriptions of prior work. This review's objective is to comprehensively examine published CEA modeling methods used to evaluate colorectal cancer (CRC) screening from an aspiring modeler's perspective. It compares existing models, highlighting the importance of precise modeling method descriptions and essential factors when modeling CRC progression.

Methods: MEDLINE, EMBASE, Web of Science, and Scopus electronic databases were used. The Consolidated Health Economic Evaluation Reporting Standards statement and data items from previous systematic reviews formed a template to extract relevant data. Specific focus included model type, natural history, appropriate data sources, and survival analysis.

Results: Seventy-eight studies, with 52 unique models were found. Twelve previously published models were reported in 39 studies, with 39 newly developed models. CRC progression from the onset was commonly modeled, with only 6 models not including it as a model component.

Conclusions: Modeling methods needed to simulate CRC progression depend on the natural history structure and research requirements. For aspiring modelers, accompanying models with clear overviews and extensive modeling assumption descriptions are beneficial. Open-source modeling would also allow model replicability and result in appropriate decisions suggested for CRC screening programs.